inflation v/s commodity market

8/2/2019 Inflation v/s commodity market

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INFLATION & COMMODITY MARKET

PROJECT DONE BY,

BHAVIKA THAKKER

PGDBM



OBJECTIVE OF THE PROJECT

Testing the market efficiency

If the market are efficient than predicting spotprices through futures

Establishing relation between inflation & economic indicators like money supply, volume incommodity market, crude oil & historical inflation



Testing The Market Efficiency

VOLUME IS INCREASING

RETURNS ARE INCREASING

INFLATION IS RISING

GOVERNMENT IS INTERVENING



Volume analysis

volume ananlysis

0.00

100000.00

200000.00

300000.00400000.00

500000.00

D e c 0 3

M a y

O c t 0 4

M a r 0 5

A u

g 0 5

J a n 0 6

J u

n 0 6

N o v 0

6

A p r 0 7

S e p 0 7

F e b 0 8

Month

v o l u m e

MCX

NCDEX



Comparing the risk and returns of different asset classes

MEAN (Annual) CAGR STD DEV (Annual)

G-SEC 10 YR BOND 7.51%

MCX GOLD 21.90% 21.67% 15.94%

MCX COPPER 30.74% 29.04% 30.94%

MCX CRUDE 31.34% 30.81% 26.85%

NIFTY 31.18% 31.60% 23.47%

SENSEX 29.62% 29.36% 24.48%

SILVER 30.74% 29.04% 30.94%



0.005000.00

10000.0015000.0020000.0025000.0030000.0035000.0040000.0045000.00

1 / 3 1 / 2 0 0 5

5 / 3 1 / 2 0 0 5

9 / 3 0 / 2 0 0 5

1 / 3 1 / 2 0 0 6

5 / 3 1 / 2 0 0 6

9 / 3 0 / 2 0 0 6

1 / 3 1 / 2 0 0 7

5 / 3 1 / 2 0 0 7

9 / 3 0 / 2 0 0 7

1 / 3 1 / 2 0 0 8

5 / 3 1 / 2 0 0 8

coppersilver

gold

crude

sensexnifty



INCREASE IN WPIWPI

050

100150200250

4 / 3 0 / 1 9 9 4

2 / 2 8 / 1 9 9 5

1 2 / 3 1 / 1 9 9 5

1 0 / 3 1 / 1 9 9 6

8 / 3 1 / 1 9 9 7

6 / 3 0 / 1 9 9 8

4 / 3 0 / 1 9 9 9

2 / 2 9 / 2 0 0 0

1 2 / 3 1 / 2 0 0 0

1 0 / 3 1 / 2 0 0 1

8 / 3 1 / 2 0 0 2

6 / 3 0 / 2 0 0 3

4 / 3 0 / 2 0 0 4

2 / 2 8 / 2 0 0 5

1 2 / 3 1 / 2 0 0 5

1 0 / 3 1 / 2 0 0 6

8 / 3 1 / 2 0 0 7

TIME

W P I



METHOD FOLLWED FOR TESTINGMARKET EFFICIENCY

COINTEGRATION

Definition : If there exist a stationarylinear combination of non stationaryrandom variables, the variables combinedare said to be cointegrated.

http://summers/Unit%20Roots%20and%20Cointegrated%20Series.htm

http://summers/Unit%20Roots%20and%20Cointegrated%20Series.htm



DATA COLLECTIONOne three month contract of refined soya oil is taken (Apr, 08) fromNCDEX database. Three time series of spot and future is been constructedfor the analysis.

Series 1: First series is made with the seven days lag of future prices byassuming market players know what would be the demand supplyscenario after the seven days and can predict spot prices in precisemanner.

Series 2: Second series is constructed with the fourteen days lag of future prices to check whether market players know what would be thedemand supply scenario after the fourteen days and are they able to

predict spot prices or no?Series 3 & 4: Similarly third and fourth time series are constructed withthe one and two month lag of future prices respectively to check the longterm and short term equilibrium between spot and future prices.



ProcedureDetermine whether yt and xt are I(1). This is equivalent to

determining whether or not they have unit roots

Provided they are both I(1), estimate the parameters of thecointegrating relation

Test to find least squares residual appears to be I(0)

If the series is cointegrated we put restriction on vector to seethe future prices are unbiased indicator of spot . We check thisbecause high speculation leads to the increase in future priceswhich may not be the pure or unbiased indicator of spot

After checking for restriction if we get the cointegration

relationship we fit the model into VECM to get the cointegratingcoefficients.

Coefficients can be used for forecasting the spot prices in thattime horizon.



FINDINGS..For all the three series we got cointegration and aftergetting the cointegration linear trends were established andcointegrating vectors were measured.

Only for first series equilibrium was obtained even afterputting the restriction on b coefficients. This can beinterpreted as that the future prices are unbiased predictorof spot in seven days horizon i.e. future market player hasthe idea of what would be the spot price in seven days.

When market is in this equilibrium we can predict the spotprice seven days in advance



Apr 08 contract: prediction sevendays in advance

-100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71

spot pricespredicted



Mar 08 Contract: Prediction SevenDays Prior:

-

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76

Series1

Series2

(Series 1 represents actual spot prices and series 2 predicted values)



INFLATION

Inflation is a rise in the general level of prices over time. It may also refer to a

rise in the prices of a specific set of goodsor services. In either case, it is measuredas the percentage rate of change of aprice index

http://en.wikipedia.org/wiki/Price_level


http://en.wikipedia.org/wiki/Price_index

http://en.wikipedia.org/wiki/Price_index





Causes of inflation

Demand-pull inflation

Cost-push inflation

Built-in inflation

http://en.wikipedia.org/wiki/Demand-pull_inflation

http://en.wikipedia.org/wiki/Cost-push_inflation

http://en.wikipedia.org/wiki/Built-in_inflation












Causes of current inflation rise:

Wage pressure

Commodity prices and expectations

Imported Inflation



GROWTH IN INFLATION INDEX

-2

0

2

4

6

8

10

12

14

1 / 3 1 / 2 0 0 0

6 / 3 0 / 2 0 0 0

1 1 / 3 0 / 2 0 0

4 / 3 0 / 2 0 0 1

9 / 3 0 / 2 0 0 1

2 / 2 8 / 2 0 0 2

7 / 3 1 / 2 0 0 2

1 2 / 3 1 / 2 0 0

5 / 3 1 / 2 0 0 3

1 0 / 3 1 / 2 0 0

3 / 3 1 / 2 0 0 4

8 / 3 1 / 2 0 0 4

1 / 3 1 / 2 0 0 5

6 / 3 0 / 2 0 0 5

1 1 / 3 0 / 2 0 0

4 / 3 0 / 2 0 0 6

9 / 3 0 / 2 0 0 6

2 / 2 8 / 2 0 0 7

7 / 3 1 / 2 0 0 7

1 2 / 3 1 / 2 0 0

5 / 3 1 / 2 0 0 8

US CPI

CHINA CPI

FRANCE CPI

JAPAN CPI

INDIA WPI



EFFECT OF INFLATION ON GOLD

0

2000

4000

6000

8000

10000

12000

14000

1 1 / 3 0 / 2 0 0 3

3 / 3 1 / 2 0 0 4

7 / 3 1 / 2 0 0 4

1 1 / 3 0 / 2 0 0 4

3 / 3 1 / 2 0 0 5

7 / 3 1 / 2 0 0 5

1 1 / 3 0 / 2 0 0 5

3 / 3 1 / 2 0 0 6

7 / 3 1 / 2 0 0 6

1 1 / 3 0 / 2 0 0 6

3 / 3 1 / 2 0 0 7

7 / 3 1 / 2 0 0 7

1 1 / 3 0 / 2 0 0 7

3 / 3 1 / 2 0 0 8

wpi

gold



EFFECT OF INFLATION ON NSE

-200

-150

-100

-50

0

50

100

150

200

9 / 3

0 / 1 9 9 8

7 / 3

1 / 1 9 9 9

5 / 3

1 / 2 0 0 0

3 / 3

1 / 2 0 0 1

1 / 3

1 / 2 0 0 2

1 1 / 3

0 / 2 0 0 2

9 / 3

0 / 2 0 0 3

7 / 3

1 / 2 0 0 4

5 / 3

1 / 2 0 0 5

3 / 3

1 / 2 0 0 6

1 / 3

1 / 2 0 0 7

1 1 / 3

0 / 2 0 0 7

0

1

2

3

4

5

67

8

9

10

nse

inflation



CORELATION MATRIX

WPI(-1) VOLUME(-1) M3(-1) WTI(-1)

WPI(-1) 1 0.93094256 0.98165113 0.90283515

VOLUME(-1) 0.93094256 1 0.90283515 0.871265068

M3(-1)

0.98165113

0.909206176

1

0.904038656

WTI(-1) 0.90283515 0.871265068 0.904038656 1



Establishing relation forinflation

Regression testing for the hypothesis that mispricing of commodity futures contracts leads to inflation is performedby multiple regression

WPI is regressed on the volumes of trade in leading twofutures exchanges in India (NCDEX and MCX), moneysupply (M3) and oil prices.

We took WPI as dependent variable. The independentvariables are one lagged WPI, WTI, money supply (M3)and volume. One lag is taken because WPI figures that weget is it self a laggard variable.



Multiple regression results



INFERENCES

Futures price contains better information about the supplyand demand of the commodity when it gets closer to itsmaturity or at least in seven days horizonFor seven days horizon using the cointegration equationcoefficients spot price were forecasted which showed thesame trend as of actual valuesThe regression model is significant as suggested by F test.It suggests that the inflation is persistent as it depends onits past values (approx. 76.32%).Rising crude prices showed next higher effect on theinflation.The growth in volume of trade and growth in money supplyare not so important determinants of inflation as theircoefficients are very small.



THANK YOU

inflation v/s commodity market

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